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Numerical Evaluation of SDPA (Semidefinite Programming Algorithm)

  • Katsuki Fujisawa
  • Mituhiro Fukuda
  • Masakazu Kojima
  • Kazuhide Nakata
Part of the Applied Optimization book series (APOP, volume 33)

Abstract

SDPA (SemiDefmite Programming Algorithm) is a C++ implementation of a Mehrotra-type primal-dual predictor-corrector interior-point method for solving the standard form semidefinite program and its dual. We report numerical results of large scale problems to evaluate its performance, and investigate how major time-consuming parts of SDPA vary with the problem size, the number of constraints and the sparsity of data matrices.

Keywords

Search Direction Conjugate Gradient Method Semidefinite Program Minimum Eigenvalue Maximum Clique Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2000

Authors and Affiliations

  • Katsuki Fujisawa
  • Mituhiro Fukuda
  • Masakazu Kojima
  • Kazuhide Nakata

There are no affiliations available

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